The proposed research is designed to assess hippocampal function, as characterized by a computational theory of the hippocampus, in children with Down syndrome (DS). The tasks used to assess function are motivated by the hypothesis that the hippocampus is best distinguished as a fast encoder of conjunctive stimuli. This fast, conjunctive theory is supported by both behavioral evidence and a neural network model that incorporates known properties of hippocampal neuroanatomy and physiology. It is hypothesized that a new, hippocampal version of the Eyeblink Classical Conditioning Task (HECC) designed for the proposed studies, will isolate hippocampal function in accordance with the theory and thus is well-suited to assess children with DS. While both toddlers and adolescents with DS and DS mouse models have shown deficits in various tasks thought to tap hippocampal function, these results may be misleading because past tasks also heavily recruit other brain areas that may be impaired in DS, such as the prefrontal cortex or areas related to expressive language. Thus, the HECC will provide more conclusive evidence concerning hippocampal function in this population. It is hypothesized that children with DS and a control group will perform similarly on a delay version of the Eyeblink Classical Conditioning Task (DECC) that taps cerebellar function, but will show performance differences on the HECC. In addition, an analysis of the hypothesized performance deficits will be conducted through a neural network model that incorporates neuroanatomical and physiological abnormalities known to be present in DS.